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Wireless Networks
Published in: Wireless Networks 8/2020

11-01-2020

Reverse logistics optimization of an industrial air conditioner manufacturing company for designing sustainable supply chain: a fuzzy hybrid multi-criteria decision-making approach

Authors: Sadia Samar Ali, Turan Paksoy, Belkız Torğul, Rajbir Kaur

Published in: Wireless Networks | Issue 8/2020

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Abstract

Magnified resource consumption and depletion of natural resources calls for non-flexible or strict regulations and penalties on industrial operations, increased rate of processing and reuse of waste material as a substitute for raw material and political and legal interventions at global scale. Product recovery involves reuse, repair, refurbishing, remanufacturing and materials recycling, requires an efficient network design known as reverse logistic network and offers economical benefits in terms of fewer procurement of raw material, inventory management and less disposal. In current study, a mixed integer linear programming model designed on a multi-stage reverse logistics network for product recovery is proposed which considers different recovery options-product remanufacturing, component reprocessing and material recycling for sustainable outcomes. The model is designed to find optimal solutions for fulfilling demand and revenue needs by focusing on strategic locations for collection centers, reprocessing centers, remanufacturing plants and transportation options and simultaneously achieving sustainability goals. The model is applied on an Indian based manufacturing unit of a Saudi Arabian Industrial Air conditioner manufacturing organization and the case is presented here. The model is converted into a multi-objective programming model in accordance with the importance of each objective suiting the business needs. All relevant objective functions are evaluated using BWM, AHP and FAHP methods to obtain weights for integration into a fuzzy linear programming model which eventually provides three separate results. The model applied has originality and uniqueness for applications to solve multi-objective problems under uncertain environment and tends to strike a balance between economic and environmental objectives. The study provides for a base for further scope covering uncertainty about the amount and quality of returned products and even can be implemented by practitioners and academics for making a significant contribution in improving the efficiency of supply chains.
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Literature
1.
2.
Zhen, L., Huang, L., & Wang, W. (2019). Green and sustainable closed-loop supply chain network design under uncertainty. Journal of Cleaner Production, 227, 1195–1209.
3.
Ramos, T. R. P., Gomes, M. I., & Barbosa-Póvoa, A. P. (2014). Planning a sustainable reverse logistics system: Balancing costs with environmental and social concerns. Omega, 48, 60–74.
4.
Alfonso-Lizarazo, E. H., Montoya-Torres, J. R., & Gutiérrez-Franco, E. (2013). Modeling reverse logistics process in the agro-industrial sector: The case of the palm oil supply chain. Applied Mathematical Modelling, 37(23), 9652–9664.MATH
5.
Keyvanshokooh, E., Fattahi, M., Seyed-Hosseini, S. M., & Tavakkoli-Moghaddam, R. (2013). A dynamic pricing approach for returned products in integrated forward/reverse logistics network design. Applied Mathematical Modelling, 37(24), 10182–10202.MathSciNetMATH
6.
Agrawal, S., Singh, R. K., & Murtaza, Q. (2015). A literature review and perspectives in reverse logistics. Resources, Conservation and Recycling, 97, 76–92.
7.
Chen, C. L., & Lee, W. C. (2004). Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Computers & Chemical Engineering, 28(6–7), 1131–1144.
8.
Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), 637–649.MathSciNetMATH
9.
Mathivathanan, D., Govindan, K., & Haq, A. N. (2017). Exploring the impact of dynamic capabilities on sustainable supply chain firm’s performance using Grey-Analytical Hierarchy Process. Journal of Cleaner Production, 147, 637–653.
10.
United Nations Framework Convention on Climate Change; United Nations Framework Convention on Climate Change (UNFCCC). (1992) Climate Change Secretariat: Bonn, Germany.
11.
Kyoto Protocol; United Nations Framework Convention on Climate Change (UNFCCC). (1998). Climate Change Secretariat: Bonn, Germany.
12.
Copenhagen Accord; United Nations Framework Convention on Climate Change (UNFCCC). (2009). Climate Change Secretariat: Bonn, Germany.
13.
Doha Amendment to the Kyoto Protocol; United Nations Framework Convention on Climate Change (UNFCCC). (2012). Climate Change Secretariat: Bonn, Germany.
14.
Paris Agreement; United Nations Framework Convention on Climate Change (UNFCCC). (2015). Climate Change Secretariat: Bonn, Germany.
15.
Walker, H., & Preuss, L. (2008). Fostering sustainability through sourcing from small businesses: Public sector perspectives. Journal of Cleaner Production, 16(15), 1600–1609.
16.
Laari, S., Töyli, J., & Ojala, L. (2017). Supply chain perspective on competitive strategies and green supply chain management strategies. Journal of Cleaner Production, 141, 1303–1315.
17.
Depping, V., Grunow, M., van Middelaar, C., & Dumpler, J. (2017). Integrating environmental impact assessment into new product development and processing-technology selection: Milk concentrates as substitutes for milk powders. Journal of Cleaner Production, 149, 1–10.
18.
Pietrosemoli, L., & Monroy, C. R. (2013). The impact of sustainable construction and knowledge management on sustainability goals. A review of the Venezuelan renewable energy sector. Renewable and Sustainable Energy Reviews, 27, 683–691.
19.
Alemanni, M., Destefanis, F., & Vezzetti, E. (2011). Model-based definition design in the product lifecycle management scenario. International Journal of Advanced Manufacturing Technology, 52, 1–14.
20.
Cappa, F., Del Sette, F., Hayes, D., & Rosso, F. (2017). How to deliver open sustainable innovation: An integrated approach for a sustainable marketable product. Sustainability, 8(12), 1341.
21.
Geng, R., Mansouri, S. A., & Aktas, E. (2017). The relationship between green supply chain management and performance: A meta-analysis of empirical evidences in Asian emerging economies. International Journal of Production Economics, 183, 245–258.
22.
Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., & Fosso Wamba, S. (2017). World class sustainable supply chain management: Critical review and further research directions. The International Journal of Logistics Management, 28(2), 332–362.
23.
Krikke, H., Van Harten, A., & Schuur, P. (1999). Business case Oce: Reverse logistics network redesign for copiers. OR Spectrum, 21, 381–409.MATH
24.
Autry, C. W., Daugherty, P. J., & Richey, R. G. (2001). The challenge of reverse logistics in catalog retailing. International Journal of Physical Distribution & Logistics, 31(1), 26–37.
25.
Peralta, G. L., & Fontanos, P. M. (2006). E-waste issues and measures in the Philippines. The Journal of Material Cycles and Waste Management, 8(1), 34–39.
26.
Srivastava, S. K., & Srivastava, R. K. (2006). Managing product returns for reverse logistics. International Journal of Physical Distribution & Logistics, 36(7), 524–546.
27.
Xiaofeng, X., & Tijun, F. (2009). Forecast for the amount of returned products based on wave function. International Conference on Information Management, Innovation Management and Industrial Engineering, 2, 324–327.
28.
Lau, K. H., & Wang, Y. (2009). Reverse logistics in the electronic industry of China: A case study. Supply Chain Management, 14(6), 447–465.
29.
Janes, B., Schuur, P., & De Brito, M. P. (2010). A reverse logistics diagnostic tool: the case of the consumer electronics industry. International Journal of Advanced Manufacturing Technolgy, 47(5–8), 495–513.
30.
Chiou, C. Y., Chen, H. C., Yu, C. T., & Yeh, C. Y. (2012). Consideration factors of reverse logistics implementation-A case study of Taiwan’s electronics industry. Procedia-Social and Behavioral Sciences, 40, 375–381.
31.
Potdar, A., & Rogers, J. (2012). Reason-code based model to forecast product returns. Foresight, 14(2), 105–120.
32.
Achillas, C., Vlachokostas, C., Aidonis, D., Moussiopoulos, Ν., Iakovou, E., & Banias, G. (2010). Optimising reverse logistics network to support policy-making in the case of electrical and electronic equipment. Waste Management, 30(12), 2592–2600.
33.
Kissling, R., Fitzpatrick, C., Boeni, H., Luepschen, C., Andrew, S., & Dickenson, J. (2012). Definition of generic re-use operating models for electrical and electronic equipment. Resources, Conservation and Recycling, 65, 85–99.
34.
Krapp, M., Nebel, J., & Sahamie, H. (2013). Using forecasts and managerial accounting information to enhance closed-loop supply chain management. OR Spectrum, 35(4), 975–1007.MathSciNetMATH
35.
Ravi, V., & Shankar, R. (2004). Analysis of interactions among the barriers of reverse logistics. Technological Forecasting and Social Change, 72(8), 1011–1029.
36.
González-Torre, P., Álvarez, M., Sarkis, J., & Adenso-Dııaz, B. (2010). Barriers to the implementation of environmentally oriented reverse logistics: Evidence from the automotive industry sector. British Journal of Management, 21(4), 889–904.
37.
Tognetti, A., Grosse-Ruyken, P. T., & Wagner, S. M. (2015). Green supply chain network optimization and the trade-off between environmental and economic objectives. International Journal of Production Economics, 170, 385–392.
38.
Kannan, D., Diabat, A., Alrefaei, M., Govindan, K., & Yong, G. (2012). A carbon footprint based reverse logistics network design model. Resources, Conservation and Recycling, 67, 75–79.
39.
Matar, N., Jaber, M. Y., & Searcy, C. (2014). A reverse logistics inventory model for plastic bottles. The International Journal of Logistics Management, 25(2), 315–333.
40.
41.
Paydar, M. M., & Olfati, M. (2018). Designing and solving a reverse logistics network for polyethylene terephthalate bottles. Journal of Cleaner Production, 195, 605–617.
42.
Rogers, D. S., & Tibben-Lembke, R. (1999). Going backwards: reverse logistics trends and practices. Reno, NV: Reverse Logistics Executive Council.
43.
Abraham, N. (2011). The apparel aftermarket in India—A case study focusing on reverse logistics. Journal of Fashion Marketing and Management, 15(2), 211–227.
44.
Guo, J., Wang, X., Fan, S., & Gen, M. (2017). Forward and reverse logistics network and route planning under the environment of low-carbon emissions: A case study of Shanghai fresh food E-commerce enterprises. Computers & Industrial Engineering, 106, 351–360.
45.
Louwers, D., Kip, B. J., Peters, E., Souren, F., & Flapper, S. D. P. (1999). A facility location allocation model for reusing carpet material. Computer Ind Engineering, 36(4), 855–869.
46.
Realff, M. J., Ammons, J. C., & Newton, D. J. (2004). Robust reverse production system design for carpet recycling. IIE Transactions, 36(8), 767–776.
47.
Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications: A state-of-the-art survey. Berlin, Heidelberg: Springer.MATH
48.
Krikke, H. R., Bloemh of-Ruwaard, J., & Van Wassenhove, L. N. (2003). Concurrent product and closed-loop supply chain design with an application to refrigerators. International Journal of Production Research, 41(16), 3689–3719.MATH
49.
Xie, X., Zeng, S., Peng, Y., & Tam, C. (2013). What affects the innovation performance of small and medium-sized enterprises in China? Innovation, 15(3), 271–286.
50.
Alumur, S. A., Nickel, S., Saldanha-da-Gama, F., & Verter, V. (2012). Multi-period reverse logistics network design. European Journal of Operational Research, 220(1), 67–78.MathSciNetMATH
51.
Liang, T. F., & Cheng, H. W. (2009). Application of fuzzy sets to manufacturing/distribution planning decisions with multi-product and multi-time period in supply chains. Expert Systems with Applications, 36(2), 3367–3377.
52.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multi objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.
53.
Krikke, H. (2011). Impact of closed-loop network configurations on carbon footprints: a case study in copiers. Resource Conservation Recycling, 55(12), 1196–1205.
54.
Tosarkani, B. M., & Amin, S. H. (2019). An environmental optimization model to configure a hybrid forward and reverse supply chain network under uncertainty. Computers & Chemical Engineering, 121, 540–555.
55.
Azadi, M., Shabani, A., Khodakarami, M., & Saen, R. F. (2014). Planning in feasible region by two-stage target-setting DEA methods: An application in green supply chain management of public transportation service providers. Transportation Research Part E: Logistics and Transportation Review, 70, 324–338.
56.
Francesco, M. D., Crainic, T. G., & Zuddas, P. (2009). The effect of multi-scenario policies on empty container repositioning. Transportation Research Part E, 45(5), 758–770.
57.
Meng, Q., & Wang, S. (2011). Liner shipping service network design with empty container repositioning. Transportation Research Part E, 47(5), 695–708.
58.
Sasikumar, P., Kannan, G., & Haq, A. N. (2010). A multi-echelon reverse logistics network design for product recovery—A case of truck tire remanufacturing. International Journal of Advanced Manufacturing Technology, 49(9–12), 1223–1234.
59.
Guide, V. D. R., Jr., & Pentico, D. W. (2003). A hierarchical decision model for re-manufacturing and re-use. International Journal of Logistics: Research & Applications, 6(1–2), 29–35.
60.
Geyer, R., & Blass, V. D. (2010). The economics of cell phone reuse and recycling. International Journal of Advanced Manufacturing Technology, 47(5–8), 515–525.
61.
Kannan, G., Palaniappan, M., Zhu, Q., & Kannan, D. (2012). Analysis of third-party reverse logistics provider using interpretive structural modeling. International Journal of Production Research, 140(1), 204–211.
62.
63.
Fleischmann, M., Beullens, P., Dekker, R., Bloemhof-Ruwaard, J. M., & Wassenhove L. N. Van (2001). The impact of product recovery on logistics network design, 10, 156–173.
64.
Rao, P., & Holt, D. (2005). Do green supply chains lead to competitiveness and economic performance? The International Journal of Operations & Production Management, 25, 898–916.
65.
Shang, K. C., Lu, C. S., & Li, S. (2010). A taxonomy of green supply chain management capability among electronics-related manufacturing firms in Taiwan. Journal of Environmental Management, 91, 1218–1226.
66.
Zhu, Q., Sarkis, J., & Lai, K. (2007). Green supply chain management: pressures, practices and performance within Chinese automobile industry. Journal of Cleaner Production, 15, 1041–1052.
67.
Kannan, D., Diabat, A., & Shankar, K. M. (2012). Analyzing the drivers of end-of-life tire management using interpretive structural modeling (ISM). International Journal of Advanced Manufacturing Technology, 72(9–12), 1603–1614.
68.
Mittal, V. K., & Sangwan, K. S. (2013). Assessment of hierarchy and inter-relationships of barriers to environmentally conscious manufacturing adoption. World Journal of Science, Technology and Sustainable Development, 10(4), 297–307.
69.
Kilic, H. S., Cebeci, U., & Ayhan, M. B. (2015). Reverse logistics system design for the waste of electrical and electronic equipment (WEEE) in Turkey. Resources, Conservation and Recycling, 95, 120–132.
70.
Varsei, M., & Polyakovskiy, S. (2017). Sustainable supply chain network design: A case of the wine industry in Australia. Omega, 66, 236–247.
71.
Liao, T. Y. (2018). Reverse logistics network design for product recovery and remanufacturing. Applied Mathematical Modelling, 60, 145–163.MathSciNetMATH
72.
Kuşakcı, A. O., Ayvaz, B., Cin, E., & Aydın, N. (2019). Optimization of reverse logistics network of End of Life Vehicles under fuzzy supply: A case study for Istanbul Metropolitan Area. Journal of Cleaner Production, 215, 1036–1051.
73.
Kogg, B., & Mont, O. (2012). Environmental and social responsibility in supply chains: The practise of choice and inter-organisational management. Ecological Economics, 83, 154–163.
74.
Mohanty, R. P., & Prakash, A. (2014). Green supply chain management practices in India: An empirical study. Production Planning & Control, 25(16), 1322–1337.
75.
Perotti, S., Micheli, G. J., & Cagno, E. (2015). Motivations and barriers to the adoption of green supply chain practices among 3PLs. International Journal of Logistics Systems and Management, 20(2), 179–198.
76.
Abdessalem, M., Hadj-Alouane, A. B., & Riopel, D. (2012). Decision modelling of reverse logistics systems: selection of recovery operations for end-of-life products. Interantional Journal of Logistics Systems and Management, 13(2), 139.
77.
Ali, S. S., Madaan, J., Chan, F. T. S., & Kannan, S. (2013). Inventory management of perishable products: A time decay linked logistic approach. International Journal of Production Research, 51(13), 3864–3879.
78.
79.
Thierry, M., Salomon, M., Nunen, J., & Wassenhove, L. (1995). Strategic issues in product recovery management. California Management Review, 37(2), 114–135.
80.
Tibben-Lembke, R. S., & Rogers, D. S. (2002). Differences between forward and reverse logistics in a retail environment. Supply Chain Management: An International Journal, 7, 271–282.
81.
De Brito, M., & Dekker, R. (2002). Reverse logistics—A framework. Rotterdam: Erasmus University Rotterdam, Econometric Institute, Econometric Institute Report.
82.
Mutha, A., & Pokharel, S. (2009). Strategic network design for reverse logistics and remanufacturing using new and old product modules. Computers & Industrial Engineering, 56(1), 334–346.
83.
Jayaraman, V., & Luo, Y. (2007). Creating competitive advantages through new value creation: A reverse logistics perspective. Academy of Management Perspectives, 21(2), 56–73.
84.
Rubio, S., Chamorro, A., & Miranda, F. J. (2008). Characteristics of the research on reverse logistics (1995–2005). International Journal of Production Research, 46, 1099–1120.MATH
85.
86.
87.
Epel, E. S., Blackburn, E. H., Lin, J., Dhabhar, F. S., Adler, N. E., Morrow, J. D., et al. (2004). Accelerated telomere shortening in response to life stress. Proceedings of the National Academy of Sciences, 101(49), 17312–17315.
88.
Fleischmann, M., Krikke, H. R., Dekker, R., & Flapper, S. P. D. (2000). A characterization of logistics networks for product recovery. Omega, 28, 653–666.
89.
Jayaraman, V., Guide, V. D. R., & Srivastava, R. (1999). A closed-loop logistics model for remanufacturing. Journal of Operational Research Society, 50(5), 497–508.MATH
90.
Vahdani, B., Jolai, F., Tavakkoli-Moghaddam, R., & Mousavi, S. M. (2012). Two fuzzy possibilistic bi-objective zero one programming models for outsourcing the equipment maintenance problem. Engineering Optimization, 44(7), 801–820.MathSciNet
91.
Schrijver, A. (2003). Combinatorial optimization: Polyhedra and efficiency. Berlin: Springer.MATH
92.
Soleimani, H., & Kannan, G. (2015). A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks. Applied Mathematical Modelling, 39(14), 3990–4012.MathSciNetMATH
93.
Zeballos, L. J., Gomes, M. I., Barbosa-Povoa, A. P., & Novais, A. Q. (2012). Addressing the uncertain quality and quantity of returns in closed-loop supply chains. Computers & Chemical Engineering, 47, 237–247.
94.
Hasani, A., Zegordi, S. H., & Nikbakhsh, E. (2012). Robust closed-loop supply chain network design for perishable goods in agile manufacturing under uncertainty. International Journal of Production Research, 50(16), 4649–4669.
95.
Papageorgiou, L. G. (2009). Supply chain optimisation for the process industries: Advances and opportunities. Computers & Chemical Engineering, 33(12), 1931–1938.
96.
97.
Jamshidi, R., Fatemi Ghomi, S., & Karimi, B. (2012). Multi-objective green supply chain optimization with a new hybrid memetic algorithm using the Taguchi method. Scientia Iranica, 19(6), 1876–1886.
98.
Hu, T. L., Sheu, J. B., & Huang, K. H. (2002). A reverse logistics cost minimization model for the treatment of hazardous wastes. Transportation Research Part E: Logistics and Transportation Review, 38(6), 457–473.
99.
100.
Salema, M. I. G., Barbosa-Povoa, A. P., & Novais, A. Q. (2007). An optimization model for the design of a capacitated multi-product reverse logistic.
101.
Pishvaee, M. S., Jolai, F., & Razmi, J. (2009). A stochastic optimization model for integrated forward/reverse logistics network design. Journal of Manufacturing Systems, 28(4), 107–114.
102.
Lee, D. H., & Dong, M. (2009). Dynamic network design for reverse logistics operations under uncertainty. Transportation Research Part E: Logistics and Transportation Review, 45(1), 61–71.
103.
Lee, D. H., Dong, M., & Bian, W. (2010). The design of sustainable logistics network under uncertainty. International Journal of Production Economics, 128(1), 159–166.
104.
Pishvaee, M. S., & Torabi, S. A. (2010). A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy Sets and Systems, 161(20), 2668–2683.MathSciNetMATH
105.
Niknejad, A., & Petrovic, D. (2014). Optimisation of integrated reverse logistics networks with different product recovery routes. European Journal of Operational Research, 238(1), 143–154.MathSciNetMATH
106.
Manuel Monsreal Barrera, M., & Cruz-Mejia, O. (2014). Reverse logistics of recovery and recycling of non-returnable beverage containers in the brewery industry: A “profitable visit” algorithm. International Journal of Physical Distribution & Logistics Management, 44(7), 577–596.
107.
Choudhary, A., Sarkar, S., Settur, S., & Tiwari, M. K. (2015). A carbon market sensitive optimization model for integrated forward–reverse logistics. International Journal of Production Economics, 164, 433–444.
108.
Govindan, K., Paam, P., & Abtahi, A. R. (2016). A fuzzy multi-objective optimization model for sustainable reverse logistics network design. Ecological Indicators, 67, 753–768.
109.
Arampantzi, C., & Minis, I. (2017). A new model for designing sustainable supply chain networks and its application to a global manufacturer. Journal of Cleaner Production, 156, 276–292.
110.
John, S. T., Sridharan, R., Kumar, P. R., & Krishnamoorthy, M. (2018). Multi-period reverse logistics network design for used refrigerators. Applied Mathematical Modelling, 54, 311–331.MathSciNetMATH
111.
Eskandari-Khanghahi, M., Tavakkoli-Moghaddam, R., Taleizadeh, A. A., & Amin, S. H. (2018). Designing and optimizing a sustainable supply chain network for a blood platelet bank under uncertainty. Engineering Applications of Artificial Intelligence, 71, 236–250.
112.
Bal, A., & Satoglu, S. I. (2018). A goal programming model for sustainable reverse logistics operations planning and an application. Journal of Cleaner Production, 201, 1081–1091.
113.
Kim, J., Do Chung, B., Kang, Y., & Jeong, B. (2018). Robust optimization model for closed-loop supply chain planning under reverse logistics flow and demand uncertainty. Journal of Cleaner Production, 196, 1314–1328.
114.
115.
Govindan, K., Jafarian, A., & Nourbakhsh, V. (2018). Designing a sustainable supply chain network integrated with vehicle routing: A comparison of hybrid swarm intelligence metaheuristics. Computers & Operations Research.
116.
Wang, X., Zhao, M., & He, H. (2018). Reverse logistic network optimization research for sharing bikes. Procedia Computer Science, 126, 1693–1703.
117.
Sahebjamnia, N., Fathollahi-Fard, A. M., & Hajiaghaei-Keshteli, M. (2018). Sustainable tire closed-loop supply chain network design: Hybrid metaheuristic algorithms for large-scale networks. Journal of Cleaner Production, 196, 273–296.
118.
Qiu, Y., Ni, M., Wang, L., Li, Q., Fang, X., & Pardalos, P. M. (2018). Production routing problems with reverse logistics and remanufacturing. Transportation Research Part E: Logistics and Transportation Review, 111, 87–100.
119.
Jin, H., Song, B. D., Yih, Y., & Sutherland, J. W. (2019). A bi-objective network design for value recovery of neodymium-iron-boron magnets: A case study of the United States. Journal of Cleaner Production, 211, 257–269.
120.
121.
Rahimi, M., Ghezavati, V., & Asadi, F. (2019). A stochastic risk-averse sustainable supply chain network design problem with quantity discount considering multiple sources of uncertainty. Computers & Industrial Engineering.
122.
Zimmermann, H. J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and System, 1, 44–55.MathSciNetMATH
123.
Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Journal of Management Science, 17(4), 141–164.MathSciNetMATH
124.
Behera, S. K., & Nayak, J. R. (2011). Solution of multi-objective mathematical programming problems in fuzzy approach. International Journal on Computer Science and Engineering, 3(12), 3790.
125.
Tiwari, R. N., Dharmahr, S., & Rao, J. R. (1987). Fuzzy goal programming-an additive model. Fuzzy Sets and Systems, 24(1), 27–34.MathSciNetMATH
126.
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57.
127.
Rezaei, J., Wang, J., & Tavasszy, L. (2015). Linking supplier development to supplier segmentation using Best Worst Method. Expert Systems with Applications, 42(23), 9152–9164.
128.
Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126–130.
129.
130.
Kilic, E., Ali, S. S., Weber, G. W., & Dubey, R. (2014). A value-adding approach to reliability under preventive maintenance costs and its applications. Optimization, 63(12), 1805–1816.MathSciNetMATH
Metadata
Title
Reverse logistics optimization of an industrial air conditioner manufacturing company for designing sustainable supply chain: a fuzzy hybrid multi-criteria decision-making approach
Authors
Sadia Samar Ali
Turan Paksoy
Belkız Torğul
Rajbir Kaur
Publication date
11-01-2020
Publisher
Springer US
Published in
Wireless Networks / Issue 8/2020
Print ISSN: 1022-0038
Electronic ISSN: 1572-8196
DOI
https://doi.org/10.1007/s11276-019-02246-6

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